Gorur-Shandilya Srinivas, Hoyland Alec, Marder Eve
Volen National Center for Complex Systems and Biology Department, Brandeis University, Waltham, MA, United States.
Front Neuroinform. 2018 Nov 26;12:87. doi: 10.3389/fninf.2018.00087. eCollection 2018.
Conductance-based models of neurons are used extensively in computational neuroscience. Working with these models can be challenging due to their high dimensionality and large number of parameters. Here, we present a neuron and network simulator built on a novel automatic type system that binds object-oriented code written in C++ to objects in MATLAB. Our approach builds on the tradition of uniting the speed of languages like C++ with the ease-of-use and feature-set of scientific programming languages like MATLAB. Xolotl allows for the creation and manipulation of hierarchical models with components that are named and searchable, permitting intuitive high-level programmatic control over all parts of the model. The simulator's architecture allows for the interactive manipulation of any parameter in any model, and for visualizing the effects of changing that parameter immediately. Xolotl is fully featured with hundreds of ion channel models from the electrophysiological literature, and can be extended to include arbitrary conductances, synapses, and mechanisms. Several core features like bookmarking of parameters and automatic hashing of source code facilitate reproducible and auditable research. Its ease of use and rich visualization capabilities make it an attractive option in teaching environments. Finally, xolotl is written in a modular fashion, includes detailed tutorials and worked examples, and is freely available at https://github.com/sg-s/xolotl, enabling seamless integration into the workflows of other researchers.
基于电导的神经元模型在计算神经科学中被广泛使用。由于这些模型具有高维度和大量参数，使用它们可能具有挑战性。在这里，我们展示了一个基于新型自动类型系统构建的神经元和网络模拟器，该系统将用C++编写的面向对象代码与MATLAB中的对象绑定在一起。我们的方法建立在将C++等语言的速度与MATLAB等科学编程语言的易用性和功能集相结合的传统之上。Xolotl允许创建和操作具有可命名和可搜索组件的分层模型，从而实现对模型所有部分的直观高级编程控制。模拟器的架构允许对任何模型中的任何参数进行交互式操作，并能立即可视化更改该参数的效果。Xolotl具有来自电生理文献的数百个离子通道模型的完整功能，并且可以扩展以包括任意电导、突触和机制。诸如参数书签和源代码自动哈希等几个核心功能有助于进行可重复和可审计的研究。它的易用性和丰富的可视化功能使其在教学环境中成为一个有吸引力的选择。最后，Xolotl以模块化方式编写，包括详细的教程和示例，并且可在https://github.com/sg-s/xolotl上免费获取，能够无缝集成到其他研究人员的工作流程中。
